Single-photon emission computed tomography (SPECT) imaging has become a standard component of modern cardiology. In SPECT research (as in medical imaging generally), it has become widely accepted that advances in imaging hardware and algorithms should be guided by so-called task-based evaluation criteria, i.e., measures that reflect how the imaging technique will impact clinical decision making. In general, a human observer study is the gold standard for measuring task-based criteria; however, the expense and complexity of such studies precludes their routine use. Therefore, numerical observers-algorithms that emulate human observer performance-are now widely used as surrogates for human observers. In SPECT, one particular numerical observer, known as the channelized Hotelling observer (CHO), has come to dominate the field. The CHO is a detection algorithm that is used to approximate the human observer's performance in detecting lesions; in the case of cardiac SPECT, the lesions of interest are perfusion defects. An imaging system or algorithm can be judged by the ability of the CHO to accurately detect defects based on the images produced. SPECT researchers now rely heavily (and sometimes exclusively) on numerical observers such as the CHO, not only to validate their final results, but also as a figure of merit that guides optimization of hardware or algorithms. Because of the central role it has come to play, the CHO and its extensions have become a major research topic in their own right. In the proposed project, our goal will be to create a suite of numerical observers that will shed light on a much wider set of clinical tasks than the CHO, and we will pursue an approach that we hypothesize will be more accurate than the CHO. Therefore, the proposed research is significant because it will yield an evaluation methodology that could potentially be used very widely by the research community, underpinning the development of imaging hardware and software. We will develop a software package for image quality assessment using the proposed NO approach and distribute it freely to the research community. As a by-product of the research, the proposed project will also yield a thorough task-based evaluation of major image reconstruction algorithms, and will answer the question of which sorts of data (on the spectrum from simple phantoms to clinical data) are a sufficient foundation for numerical observers to perform as desired. The research will also yield the basis for a potential computer aided diagnostic system for cardiology. The specific aims of the research will be as follows: 1) Create a comprehensive set of imaging data and human observer scores; 2) Develop a suite of numerical observers based on our novel learning-based approach, as well as more-conventional statistical decision theory principles; 3) Compare and evaluate the numerical observers; and 4) Develop and disseminate by-products of the research program.